The present invention relates to a device and a method for monitoring a gas turbine.
The condition of gas turbines deteriorates over the operating life. In this case, the condition can deteriorate because of aging phenomena or because of individual events. Examples of aging phenomena are erosion or corrosion. An individual event can be damage from foreign matter that gets sucked in. Whereas the consequence of aging phenomena is a gradual deterioration of all components of the gas turbine, in certain circumstances to varying degrees, individual events result in rapid changes to the performance parameters of a few components.
Performance calculation programs that simulate the operating behavior of the gas turbine in an undeteriorated condition are used to monitor gas turbines. This makes it possible to determine, for every operating point, expected values for the corresponding measured variables. The deviations between the expected values and measured values, called residua, represent the basis for monitoring the gas turbine.
In the case of individual events, rapid changes are to be expected in the residua. Identifying these changes as promptly as possible is the objective of detection. In the case of positive detection, the objective of diagnostics is identifying the affected components. With both positive and negative detection, the objective of prognosis is predicting the further course of the respective parameters so as to prevent limit values from possibly being exceeded. In particular with respect to monitoring a plurality of gas turbines, e.g., fleet of engines, it is of crucial importance to automate the task of detection.
The traditional, prevailing maintenance philosophy for gas turbines consists of maintenance according to specified cycles with simultaneously monitoring of global parameters, such as, the turbine outlet temperature or specific fuel consumption. These global parameters are merely monitored with respect to specified limit values being exceeded.
Since the 1990s a change has been recognizable in the maintenance philosophy from time-based maintenance to condition-based maintenance. The condition-based maintenance requires precise knowledge of the condition of the respective gas turbine, which is supposed to be made available by so-called engine health monitoring systems (EHM systems). U.S. Pat. No. 5,105,372 describes such a system, which is based on the use of a Kalman filter. The objective of detection is not described explicitly in this system; diagnostics are carried out independent of a rapid change in the residua. No detection functions are defined. The task of prognosis is carried out with the aid of a Kalman filter. The prognosis function is limited to predicting linear trends and does not supply any information about a confidence interval of the prediction.
In the case of monitoring statically defined limit values, a malfunction is not identified until the change in the operating behavior has reached a specific level. No detection or prognosis functions are present.
In addition, artificial intelligence methods are increasingly being applied, such as, neural networks, fuzzy logic, genetic algorithms. The system described in Therkorn's “Remote Monitoring and Diagnostic for Combined-Cycle Power Plants,” for example, searches using appropriately trained neural networks for known patterns in residua and derived quantities, and triggers a detection alarm in the case of positive pattern recognition.
All systems based on neural networks must be specially trained and configured for known faults and for each individual gas turbine type.
The object of the present invention is creating an improved device and an improved method for monitoring a gas turbine.
The approach according to the invention is suitable for detection and prognosis within the framework of condition monitoring of gas turbines. In this case, the present invention is based on the knowledge that the method of Bayes' Prediction can be used advantageously in the area of gas turbine monitoring. In particular, the objective of detection can be attained by the use of the method of Bayes' Prediction.
The present invention creates a device for monitoring a gas turbine having the following features:
a receiver for receiving condition values of the gas turbine; and
an analytical device, which is designed to determine condition information from the condition values of the gas turbine using Bayes' Prediction.
Furthermore, the present invention creates a method for monitoring a gas turbine, which features the following steps:
receiving condition values of the gas turbine; and
determining condition information of the gas turbine from the condition values using Bayes' Prediction.
According to one embodiment, the condition values may be residua. The residua can be observed in terms of their temporal progressions and be described by so-called dynamic linear models (DLM). In this case, dynamic linear models can be used such as those described in Pole, West, Harrison: “Applied Bayesian Forecasting and Time Series Analysis,” Chapman & Hall, 1994, or in West, Harrison: “Bayesian Forecasting and Dynamic Models,” Second Edition, Springer, 1997 or in West, Harrison: “Monitoring and Adaptation in Bayesian Forecasting Models,” Journal of the American Statistical Association, September 1986, Vol. 81, No. 395, Theory and Methods, 1986. As a result, it is possible, at any point in time, to indicate an expected probability density for the next time interval.
According to one embodiment, detection of rapid changes in the residua can be achieved with the aid of so-called Bayes' factors. To this end, the probability density of a current model can be compared at any point in time to the probability density of an alternative model, whose mean value is offset by a specific amount with respect to the current model. In order to design the precision of the method in an optimum way and simultaneously minimize the number of false alarms, according to the invention, a chain of logical queries are made, which are used, among other things, to calculate cumulative Bayes' factors and the associated run lengths. The chain of logical queries for the detection of rapid changes in the residua can be developed based on the theory described in West, Harrison: “Bayesian Forecasting and Dynamic Models,” Second Edition, Springer, 1997.
Preferred exemplary embodiments of the present invention are explained in greater detail in the following, making reference to the enclosed drawings.